Housing Rental Analysis for San Francisco

In this challenge, your job is to use your data visualisation skills, including aggregation, interactive visualisations, and geospatial analysis, to find properties in the San Francisco market that are viable investment opportunities.

Instructions

Use the san_francisco_housing.ipynb notebook to visualise and analyse the real-estate data.

Note that this assignment requires you to create a visualisation by using hvPlot and GeoViews. Additionally, you need to read the sfo_neighborhoods_census_data.csv file from the Resources folder into the notebook and create the DataFrame that you’ll use in the analysis.

The main task in this Challenge is to visualise and analyse the real-estate data in your Jupyter notebook. Use the san_francisco_housing.ipynb notebook to complete the following tasks:

• Calculate and plot the housing units per year.

• Calculate and plot the average prices per square foot.

• Compare the average prices by neighbourhood.

• Build an interactive neighbourhood map.

• Compose your data story.

Calculate and Plot the Housing Units per Year

For this part of the assignment, use numerical and visual aggregation to calculate the number of housing units per year, and then visualise the results as a bar chart. To do so, complete the following steps:

1. Use the groupby function to group the data by year. Aggregate the results by the mean of the groups.

2. Use the hvplot function to plot the housing_units_by_year DataFrame as a bar chart. Make the x-axis represent the year and the y-axis represent the housing_units.

3. Style and format the line plot to ensure a professionally styled visualisation.

4. Note that your resulting plot should appear similar to the following image:

   

5. Answer the following question:

   • What’s the overall trend in housing units over the period that you’re analysing?

Calculate and Plot the Average Sale Prices per Square Foot

For this part of the assignment, use numerical and visual aggregation to calculate the average prices per square foot, and then visualise the results as a bar chart. To do so, complete the following steps:

1. Group the data by year, and then average the results. What’s the lowest gross rent that’s reported for the years that the DataFrame includes?

2. Create a new DataFrame named prices_square_foot_by_year by filtering out the “housing_units” column. The new DataFrame should include the averages per year for only the sale price per square foot and the gross rent.

3. Use hvPlot to plot the prices_square_foot_by_year DataFrame as a line plot.

   Hint This single plot will include lines for both sale_price_sqr_foot and gross_rent.

4. Style and format the line plot to ensure a professionally styled visualisation.

5. Note that your resulting plot should appear similar to the following image:

   

6. Use both the prices_square_foot_by_year DataFrame and interactive plots to answer the following questions:

   • Did any year experience a drop in the average sale price per square foot compared to the previous year?

   • If so, did the gross rent increase or decrease during that year?

Compare the Average Sale Prices by Neighbourhood

For this part of the assignment, use interactive visualisations and widgets to explore the average sale price per square foot by neighbourhood. To do so, complete the following steps:

1. Create a new DataFrame that groups the original DataFrame by year and neighbourhood. Aggregate the results by the mean of the groups.

2. Filter out the “housing_units” column to create a DataFrame that includes only the sale_price_sqr_foot and gross_rent averages per year.

3. Create an interactive line plot with hvPlot that visualises both sale_price_sqr_foot and gross_rent. Set the x-axis parameter to the year (x="year"). Use the groupby parameter to create an interactive widget for neighbourhood.

4. Style and format the line plot to ensure a professionally styled visualisation.

5. Note that your resulting plot should appear similar to the following image:

   

6. Use the interactive visualisation to answer the following question:

   • For the Anza Vista neighbourhood, is the average sale price per square foot for 2016 more or less than the price that’s listed for 2012?

Build an Interactive Neighbourhood Map

For this part of the assignment, explore the geospatial relationships in the data by using interactive visualisations with hvPlot and GeoViews. To build your map, use the sfo_data_df DataFrame (created during the initial import), which includes the neighbourhood location data with the average prices. To do all this, complete the following steps:

1. Read the neighbourhood_coordinates.csv file from the Resources folder into the notebook, and create a DataFrame named neighbourhood_locations_df. Be sure to set the index_col of the DataFrame as “Neighbourhood”.

2. Using the original sfo_data_df Dataframe, create a DataFrame named all_neighbourhood_info_df that groups the data by neighbourhood. Aggregate the results by the mean of the group.

3. Review the two code cells that concatenate the neighbourhood_locations_df DataFrame with the all_neighbourhood_info_df DataFrame. Note that the first cell uses the Pandas concat function to create a DataFrame named all_neighbourhoods_df. The second cell cleans the data and sets the “Neighbourhood” column. Be sure to run these cells to create the all_neighbourhoods_df DataFrame, which you’ll need to create the geospatial visualisation.

4. Using hvPlot with GeoViews enabled, create a points plot for the all_neighbourhoods_df DataFrame. Be sure to do the following:

   • Set the size parameter to “sale_price_sqr_foot”.

   • Set the color parameter to “gross_rent”.

   • Set the frame_width parameter to 700.

   • Set the frame_height parameter to 500.

   • Include a descriptive title.

     Note that your resulting plot should appear similar to the following image:

     

5. Use the interactive map to answer the following question:

   • Which neighbourhood has the highest gross rent, and which has the highest sale price per square foot?

Compose Your Data Story

Based on the visualisations that you created, answer the following questions:

• How does the trend in rental income growth compare to the trend in sales prices? Does this same trend hold true for all the neighbourhoods across San Francisco?

• What insights can you share with your company about the potential one-click, buy-and-rent strategy that they're pursuing? Do neighbourhoods exist that you would suggest for investment, and why?

# Import the required libraries and dependencies
import pandas as pd
import hvplot.pandas
from pathlib import Path

Import the data

# Using the read_csv function and Path module, create a DataFrame 
# by importing the sfo_neighbourhoods_census_data.csv file from the Resources folder
sfo_data_df = pd.read_csv(Path('Resources/sfo_neighbourhoods_census_data.csv'))

# Review the first and last five rows of the DataFrame
display(sfo_data_df.head())
display(sfo_data_df.tail())

	year	neighbourhood	sale_price_sqr_foot	housing_units	gross_rent
0	2010	Alamo Square	291.182945	372560	1239
1	2010	Anza Vista	267.932583	372560	1239
2	2010	Bayview	170.098665	372560	1239
3	2010	Buena Vista Park	347.394919	372560	1239
4	2010	Central Richmond	319.027623	372560	1239

	year	neighbourhood	sale_price_sqr_foot	housing_units	gross_rent
392	2016	Telegraph Hill	903.049771	384242	4390
393	2016	Twin Peaks	970.085470	384242	4390
394	2016	Van Ness/ Civic Center	552.602567	384242	4390
395	2016	Visitacion Valley	328.319007	384242	4390
396	2016	Westwood Park	631.195426	384242	4390

---

Calculate and Plot the Housing Units per Year

For this part of the assignment, use numerical and visual aggregation to calculate the number of housing units per year, and then visualise the results as a bar chart. To do so, complete the following steps:

1. Use the groupby function to group the data by year. Aggregate the results by the mean of the groups.

2. Use the hvplot function to plot the housing_units_by_year DataFrame as a bar chart. Make the x-axis represent the year and the y-axis represent the housing_units.

3. Style and format the line plot to ensure a professionally styled visualisation.

4. Note that your resulting plot should appear similar to the following image:

   

5. Answer the following question:

   • What’s the overall trend in housing units over the period that you’re analysing?

Step 1: Use the groupby function to group the data by year. Aggregate the results by the mean of the groups.

# Create a numerical aggregation that groups the data by the year and then averages the results.
housing_units_by_year = sfo_data_df.groupby('year').mean()

# Review the DataFrame
housing_units_by_year

	sale_price_sqr_foot	housing_units	gross_rent
year
2010	369.344353	372560.0	1239.0
2011	341.903429	374507.0	1530.0
2012	399.389968	376454.0	2324.0
2013	483.600304	378401.0	2971.0
2014	556.277273	380348.0	3528.0
2015	632.540352	382295.0	3739.0
2016	697.643709	384242.0	4390.0

Step 2: Use the hvplot function to plot the housing_units_by_year DataFrame as a bar chart. Make the x-axis represent the year and the y-axis represent the housing_units.

Step 3: Style and format the line plot to ensure a professionally styled visualisation.

# Create a visual aggregation explore the housing units by year
housing_units_by_year.hvplot.bar(
    color='blue', y='housing_units',
    ylabel='Housing Units', xlabel='Year',
    width=900, height=400,
    ylim=(365000, 385999),
    label = 'Housing units in San Fransisco from 2010 to 2016'
).opts(
    yformatter='%.0f',
    fontsize={
    'title': 16, 
    'labels': 14,
    'xticks': 11, 
    'yticks': 11,
    }
)

Step 5: Answer the following question:

Question: What is the overall trend in housing_units over the period being analysed?

Answer: Housing units trend has increased every year throughout the 2010 to 2016 period.

---

Calculate and Plot the Average Sale Prices per Square Foot

For this part of the assignment, use numerical and visual aggregation to calculate the average prices per square foot, and then visualise the results as a bar chart. To do so, complete the following steps:

1. Group the data by year, and then average the results. What’s the lowest gross rent that’s reported for the years that the DataFrame includes?

2. Create a new DataFrame named prices_square_foot_by_year by filtering out the “housing_units” column. The new DataFrame should include the averages per year for only the sale price per square foot and the gross rent.

3. Use hvPlot to plot the prices_square_foot_by_year DataFrame as a line plot.

   Hint This single plot will include lines for both sale_price_sqr_foot and gross_rent.

4. Style and format the line plot to ensure a professionally styled visualisation.

5. Note that your resulting plot should appear similar to the following image:

   

6. Use both the prices_square_foot_by_year DataFrame and interactive plots to answer the following questions:

   • Did any year experience a drop in the average sale price per square foot compared to the previous year?

   • If so, did the gross rent increase or decrease during that year?

Step 1: Group the data by year, and then average the results.

# Create a numerical aggregation by grouping the data by year and averaging the results
prices_square_foot_by_year = sfo_data_df.groupby('year').mean()

# Review the resulting DataFrame
prices_square_foot_by_year

	sale_price_sqr_foot	housing_units	gross_rent
year
2010	369.344353	372560.0	1239.0
2011	341.903429	374507.0	1530.0
2012	399.389968	376454.0	2324.0
2013	483.600304	378401.0	2971.0
2014	556.277273	380348.0	3528.0
2015	632.540352	382295.0	3739.0
2016	697.643709	384242.0	4390.0

Question: What is the lowest gross rent reported for the years included in the DataFrame?

Answer: The lowest gross rent reported in 2010th year: 1239.0

Step 2: Create a new DataFrame named prices_square_foot_by_year by filtering out the “housing_units” column. The new DataFrame should include the averages per year for only the sale price per square foot and the gross rent.

# Filter out the housing_units column, creating a new DataFrame 
# Keep only sale_price_sqr_foot and gross_rent averages per year
prices_square_foot_by_year.drop(columns='housing_units', axis=1, inplace=True)

# Review the DataFrame
prices_square_foot_by_year

	sale_price_sqr_foot	gross_rent
year
2010	369.344353	1239.0
2011	341.903429	1530.0
2012	399.389968	2324.0
2013	483.600304	2971.0
2014	556.277273	3528.0
2015	632.540352	3739.0
2016	697.643709	4390.0

Step 3: Use hvPlot to plot the prices_square_foot_by_year DataFrame as a line plot.

Hint This single plot will include lines for both sale_price_sqr_foot and gross_rent

Step 4: Style and format the line plot to ensure a professionally styled visualisation.

# Plot prices_square_foot_by_year. 
# Inclued labels for the x- and y-axes, and a title.
prices_square_foot_by_year.hvplot.line(
    y=['sale_price_sqr_foot', 'gross_rent'],
    ylabel='Gross Rent/Sale Price Per Square Foot',
    xlabel='Year',
    ylim=(0, 4700),
    width=950, height=400,
    title = 'Sale Price Per Square Foot and Average Gross Rent - 2010 to 2016 - San Fransisco'
).opts(
    fontsize={
    'title': 15, 
    'labels': 14,
    'xticks': 11, 
    'yticks': 11,
    },
    legend_position='right'
)

Step 6: Use both the prices_square_foot_by_year DataFrame and interactive plots to answer the following questions:

Question: Did any year experience a drop in the average sale price per square foot compared to the previous year?

Answer: 2011th had a decrease in average sale price per square foot

Question: If so, did the gross rent increase or decrease during that year?

Answer: The gross rent increased that year

---

Compare the Average Sale Prices by Neighbourhood

For this part of the assignment, use interactive visualisations and widgets to explore the average sale price per square foot by neighbourhood. To do so, complete the following steps:

1. Create a new DataFrame that groups the original DataFrame by year and neighbourhood. Aggregate the results by the mean of the groups.

2. Filter out the “housing_units” column to create a DataFrame that includes only the sale_price_sqr_foot and gross_rent averages per year.

3. Create an interactive line plot with hvPlot that visualises both sale_price_sqr_foot and gross_rent. Set the x-axis parameter to the year (x="year"). Use the groupby parameter to create an interactive widget for neighbourhood.

4. Style and format the line plot to ensure a professionally styled visualisation.

5. Note that your resulting plot should appear similar to the following image:

   

6. Use the interactive visualisation to answer the following question:

   • For the Anza Vista neighbourhood, is the average sale price per square foot for 2016 more or less than the price that’s listed for 2012?

Step 1: Create a new DataFrame that groups the original DataFrame by year and neighbourhood. Aggregate the results by the mean of the groups.

# Group by year and neighbourhood and then create a new dataframe of the mean values
prices_by_year_by_neighbourhood = sfo_data_df.groupby(['year','neighbourhood']).mean()

# Review the DataFrame
prices_by_year_by_neighbourhood

		sale_price_sqr_foot	housing_units	gross_rent
year	neighbourhood
2010	Alamo Square	291.182945	372560.0	1239.0
	Anza Vista	267.932583	372560.0	1239.0
	Bayview	170.098665	372560.0	1239.0
	Buena Vista Park	347.394919	372560.0	1239.0
	Central Richmond	319.027623	372560.0	1239.0
...	...	...	...	...
2016	Telegraph Hill	903.049771	384242.0	4390.0
	Twin Peaks	970.085470	384242.0	4390.0
	Van Ness/ Civic Center	552.602567	384242.0	4390.0
	Visitacion Valley	328.319007	384242.0	4390.0
	Westwood Park	631.195426	384242.0	4390.0

397 rows × 3 columns

Step 2: Filter out the “housing_units” column to create a DataFrame that includes only the sale_price_sqr_foot and gross_rent averages per year.

# Filter out the housing_units
prices_by_year_by_neighbourhood.drop(columns='housing_units', inplace=True)

# Review the first and last five rows of the DataFrame
display(prices_by_year_by_neighbourhood.head())
display(prices_by_year_by_neighbourhood.tail())

		sale_price_sqr_foot	gross_rent
year	neighbourhood
2010	Alamo Square	291.182945	1239.0
	Anza Vista	267.932583	1239.0
	Bayview	170.098665	1239.0
	Buena Vista Park	347.394919	1239.0
	Central Richmond	319.027623	1239.0

		sale_price_sqr_foot	gross_rent
year	neighbourhood
2016	Telegraph Hill	903.049771	4390.0
	Twin Peaks	970.085470	4390.0
	Van Ness/ Civic Center	552.602567	4390.0
	Visitacion Valley	328.319007	4390.0
	Westwood Park	631.195426	4390.0

Step 3: Create an interactive line plot with hvPlot that visualises both sale_price_sqr_foot and gross_rent. Set the x-axis parameter to the year (x="year"). Use the groupby parameter to create an interactive widget for neighbourhood.

Step 4: Style and format the line plot to ensure a professionally styled visualisation.

# Use hvplot to create an interactive line plot of the average price per square foot
# The plot should have a dropdown selector for the neighbourhood
prices_by_year_by_neighbourhood.hvplot.line(
    x='year', groupby='neighbourhood',
    y=['sale_price_sqr_foot', 'gross_rent'],
    ylabel='Gross Rent/Sale Price Per Square Foot', 
    xlabel='Year', 
    width=700, height=400,
    ylim=(-210, 4800),
    label='Sale Price Per Square Foot and Average Gross Rent\n- 2010 to 2016 - By Neighbourhood\n'
).opts(
    fontsize={
    'title': 15, 
    'labels': 13,
    'xticks': 11, 
    'yticks': 11,
    },
    legend_position='right'
)

Step 6: Use the interactive visualisation to answer the following question:

Question: For the Anza Vista neighbourhood, is the average sale price per square foot for 2016 more or less than the price that’s listed for 2012?

Answer: Anza Vista's 2016 sale price per square foot less than in 2012

---

Build an Interactive Neighbourhood Map

For this part of the assignment, explore the geospatial relationships in the data by using interactive visualisations with hvPlot and GeoViews. To build your map, use the sfo_data_df DataFrame (created during the initial import), which includes the neighbourhood location data with the average prices. To do all this, complete the following steps:

1. Read the neighbourhood_coordinates.csv file from the Resources folder into the notebook, and create a DataFrame named neighbourhood_locations_df. Be sure to set the index_col of the DataFrame as “Neighbourhood”.

2. Using the original sfo_data_df Dataframe, create a DataFrame named all_neighbourhood_info_df that groups the data by neighbourhood. Aggregate the results by the mean of the group.

3. Review the two code cells that concatenate the neighbourhood_locations_df DataFrame with the all_neighbourhood_info_df DataFrame. Note that the first cell uses the Pandas concat function to create a DataFrame named all_neighbourhoods_df. The second cell cleans the data and sets the “Neighbourhood” column. Be sure to run these cells to create the all_neighbourhoods_df DataFrame, which you’ll need to create the geospatial visualisation.

4. Using hvPlot with GeoViews enabled, create a points plot for the all_neighbourhoods_df DataFrame. Be sure to do the following:

   • Set the size parameter to “sale_price_sqr_foot”.

   • Set the color parameter to “gross_rent”.

   • Set the frame_width parameter to 700.

   • Set the frame_height parameter to 500.

   • Include a descriptive title.

     Note that your resulting plot should appear similar to the following image:

     

5. Use the interactive map to answer the following question:

   • Which neighbourhood has the highest gross rent, and which has the highest sale price per square foot?

Step 1: Read the neighbourhood_coordinates.csv file from the Resources folder into the notebook, and create a DataFrame named neighbourhood_locations_df. Be sure to set the index_col of the DataFrame as “Neighbourhood”.

# Load neighbourhoods coordinates data
neighbourhood_locations_df = pd.read_csv(Path('Resources/neighbourhoods_coordinates.csv'), index_col='Neighbourhood')

# Review the DataFrame
neighbourhood_locations_df

	Lat	Lon
Neighbourhood
Alamo Square	37.791012	-122.402100
Anza Vista	37.779598	-122.443451
Bayview	37.734670	-122.401060
Bayview Heights	37.728740	-122.410980
Bernal Heights	37.728630	-122.443050
...	...	...
West Portal	37.740260	-122.463880
Western Addition	37.792980	-122.435790
Westwood Highlands	37.734700	-122.456854
Westwood Park	37.734150	-122.457000
Yerba Buena	37.792980	-122.396360

73 rows × 2 columns

Step 2: Using the original sfo_data_df Dataframe, create a DataFrame named all_neighbourhood_info_df that groups the data by neighbourhood. Aggregate the results by the mean of the group.

# Calculate the mean values for each neighbourhood
all_neighbourhood_info_df = sfo_data_df.groupby('neighbourhood').mean()

# Review the resulting DataFrame
display(all_neighbourhood_info_df.head())
display(all_neighbourhood_info_df.tail())

	year	sale_price_sqr_foot	housing_units	gross_rent
neighbourhood
Alamo Square	2013.000000	366.020712	378401.0	2817.285714
Anza Vista	2013.333333	373.382198	379050.0	3031.833333
Bayview	2012.000000	204.588623	376454.0	2318.400000
Bayview Heights	2015.000000	590.792839	382295.0	3739.000000
Bernal Heights	2013.500000	576.746488	379374.5	3080.333333

	year	sale_price_sqr_foot	housing_units	gross_rent
neighbourhood
West Portal	2012.25	498.488485	376940.75	2515.500000
Western Addition	2012.50	307.562201	377427.50	2555.166667
Westwood Highlands	2012.00	533.703935	376454.00	2250.500000
Westwood Park	2015.00	687.087575	382295.00	3959.000000
Yerba Buena	2012.50	576.709848	377427.50	2555.166667

Step 3: Review the two code cells that concatenate the neighbourhood_locations_df DataFrame with the all_neighbourhood_info_df DataFrame.

Note that the first cell uses the Pandas concat function to create a DataFrame named all_neighbourhoods_df.

The second cell cleans the data and sets the “Neighbourhood” column.

Be sure to run these cells to create the all_neighbourhoods_df DataFrame, which you’ll need to create the geospatial visualisation.

# Using the Pandas `concat` function, join the 
# neighbourhood_locations_df and the all_neighbourhood_info_df DataFrame
# The axis of the concatenation is "columns".
# The concat function will automatially combine columns with
# identical information, while keeping the additional columns.
all_neighbourhoods_df = pd.concat(
    [neighbourhood_locations_df, all_neighbourhood_info_df], 
    axis="columns",
    sort=False
)

# Review the resulting DataFrame
display(all_neighbourhoods_df.head())
display(all_neighbourhoods_df.tail())

	Lat	Lon	year	sale_price_sqr_foot	housing_units	gross_rent
Alamo Square	37.791012	-122.402100	2013.000000	366.020712	378401.0	2817.285714
Anza Vista	37.779598	-122.443451	2013.333333	373.382198	379050.0	3031.833333
Bayview	37.734670	-122.401060	2012.000000	204.588623	376454.0	2318.400000
Bayview Heights	37.728740	-122.410980	2015.000000	590.792839	382295.0	3739.000000
Bernal Heights	37.728630	-122.443050	NaN	NaN	NaN	NaN

	Lat	Lon	year	sale_price_sqr_foot	housing_units	gross_rent
Yerba Buena	37.79298	-122.39636	2012.5	576.709848	377427.5	2555.166667
Bernal Heights	NaN	NaN	2013.5	576.746488	379374.5	3080.333333
Downtown	NaN	NaN	2013.0	391.434378	378401.0	2817.285714
Ingleside	NaN	NaN	2012.5	367.895144	377427.5	2509.000000
Outer Richmond	NaN	NaN	2013.0	473.900773	378401.0	2817.285714

# Call the dropna function to remove any neighbourhoods that do not have data
all_neighbourhoods_df = all_neighbourhoods_df.reset_index().dropna()

# Rename the "index" column as "Neighbourhood" for use in the Visualisation
all_neighbourhoods_df = all_neighbourhoods_df.rename(columns={"index": "Neighbourhood"})

# Review the resulting DataFrame
display(all_neighbourhoods_df.head())
display(all_neighbourhoods_df.tail())

	Neighbourhood	Lat	Lon	year	sale_price_sqr_foot	housing_units	gross_rent
0	Alamo Square	37.791012	-122.402100	2013.000000	366.020712	378401.0	2817.285714
1	Anza Vista	37.779598	-122.443451	2013.333333	373.382198	379050.0	3031.833333
2	Bayview	37.734670	-122.401060	2012.000000	204.588623	376454.0	2318.400000
3	Bayview Heights	37.728740	-122.410980	2015.000000	590.792839	382295.0	3739.000000
5	Buena Vista Park	37.768160	-122.439330	2012.833333	452.680591	378076.5	2698.833333

	Neighbourhood	Lat	Lon	year	sale_price_sqr_foot	housing_units	gross_rent
68	West Portal	37.74026	-122.463880	2012.25	498.488485	376940.75	2515.500000
69	Western Addition	37.79298	-122.435790	2012.50	307.562201	377427.50	2555.166667
70	Westwood Highlands	37.73470	-122.456854	2012.00	533.703935	376454.00	2250.500000
71	Westwood Park	37.73415	-122.457000	2015.00	687.087575	382295.00	3959.000000
72	Yerba Buena	37.79298	-122.396360	2012.50	576.709848	377427.50	2555.166667

Step 4: Using hvPlot with GeoViews enabled, create a points plot for the all_neighbourhoods_df DataFrame. Be sure to do the following:

• Set the geo parameter to True.
• Set the size parameter to “sale_price_sqr_foot”.
• Set the color parameter to “gross_rent”.
• Set the frame_width parameter to 700.
• Set the frame_height parameter to 500.
• Include a descriptive title.

# Create a plot to analyse neighbourhood info
all_neighbourhoods_df.hvplot.points(
    'Lon', 'Lat', 
    geo= True, 
    size='sale_price_sqr_foot', 
    color='gross_rent', 
    frame_width=700, 
    frame_height=500,
    tiles=True,
)

Step 5: Use the interactive map to answer the following question:

Question: Which neighbourhood has the highest gross rent, and which has the highest sale price per square foot?

Answer: The Neighbourhoods with more darker color have the highest gross rent. The larger size of the circles of the means that those neighbourhoods have the highest sales per square foot.

Compose Your Data Story

Based on the visualisations that you created, answer the following questions:

Question: How does the trend in rental income growth compare to the trend in sales prices? Does this same trend hold true for all the neighbourhoods across San Francisco?

Answer: Rental income growth trend is consistent with the sales price trend in general in San Francisco, but not in all areas.

Question: What insights can you share with your company about the potential one-click, buy-and-rent strategy that they're pursuing? Do neighbourhoods exist that you would suggest for investment, and why?

Answer: I would suggest favoring the neighborhoods with darker colors and larger size, such as Westwood Park